检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

777 篇 会议
5 册 图书
1 篇 期刊文献

馆藏范围

783 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

466 篇 工学
- 404 篇 计算机科学与技术...
- 231 篇 软件工程
- 137 篇 信息与通信工程
- 107 篇 电气工程
- 60 篇 生物医学工程（可授...
- 58 篇 生物工程
- 37 篇 控制科学与工程
- 34 篇 电子科学与技术（可...
- 20 篇 光学工程
- 15 篇 化学工程与技术
- 14 篇 机械工程
- 14 篇 仪器科学与技术
- 12 篇 安全科学与工程
- 11 篇 动力工程及工程热...
- 10 篇 交通运输工程
173 篇 理学
- 91 篇 数学
- 65 篇 生物学
- 43 篇 物理学
- 37 篇 统计学（可授理学、...
- 28 篇 系统科学
- 15 篇 化学
98 篇 管理学
- 63 篇 图书情报与档案管...
- 39 篇 管理科学与工程(可...
- 23 篇 工商管理
63 篇 医学
- 54 篇 临床医学
- 40 篇 基础医学(可授医学...
- 31 篇 药学(可授医学、理...
13 篇 法学
- 12 篇 社会学
13 篇 农学
- 13 篇 作物学
12 篇 教育学
- 12 篇 教育学
8 篇 经济学
1 篇 军事学

主题

207 篇 machine learning
93 篇 signal processin...
87 篇 deep learning
64 篇 signal processin...
62 篇 feature extracti...
56 篇 support vector m...
50 篇 training
49 篇 machine learning...
30 篇 neural networks
21 篇 natural language...
20 篇 image processing
16 篇 computational mo...
14 篇 prediction algor...
13 篇 svm
13 篇 sentiment analys...
13 篇 data models
13 篇 convolutional ne...
12 篇 internet of thin...
12 篇 estimation
11 篇 covid-19

机构

6 篇 college of engin...
6 篇 department of co...
4 篇 xidian universit...
3 篇 department of el...
3 篇 university of du...
3 篇 peng cheng labor...
3 篇 uttaranchal inst...
3 篇 department of el...
3 篇 national laborat...
3 篇 graphic era deem...
2 篇 kl business scho...
2 篇 khulna universit...
2 篇 khulna universit...
2 篇 northwest minzu ...
2 篇 shandong univers...
2 篇 central universi...
2 篇 imt cdl ghaziaba...
2 篇 department of co...
2 篇 school of marine...
2 篇 school of comput...

作者

8 篇 wathiq mansoor
4 篇 yassine himeur
3 篇 alavikunhu panth...
3 篇 li zheng
3 篇 abigail copiaco
3 篇 jing wang
3 篇 joel alanya-belt...
3 篇 rashid ali
3 篇 shadi atalla
3 篇 abbes amira
3 篇 lei huang
2 篇 cheng xinzhou
2 篇 sherin m. yousse...
2 篇 liashchynskyi pe...
2 篇 kumar ashwani
2 篇 sarno riyanarto
2 篇 khawla a. alnajj...
2 篇 chang ming-ching
2 篇 liu ze-guo
2 篇 taleb mohammed

语言

753 篇 英文
27 篇 其他
2 篇 西班牙文
2 篇 中文

检索条件"任意字段=5th International Conference on Signal Processing and Machine Learning, SPML 2022"

共 783 条记录，以下是361-370 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Detection of Phishing Website Using Intelligent machine learning Classifiers 1

引用

5th international conference on Soft Computing and signal processing, ICSCSP 2022

作者： Pandey, Mithilesh Kumar Singh, Munindra Kumar Pal, Saurabh Tiwari, B.B. Department of Computer Applications VBS Purvanchal University Uttar Pradesh Jaunpur222001 India Department of Electronics and Communication VBS Purvanchal University Uttar Pradesh Jaunpur222001 India

ISBN: (数字)9789811986697

ISBN: (纸本)9789811986680

As a result of the rapid development of the Internet, customers’ tendency has shifted from standard shopping to e-commerce. Nowadays, the criminal party is no longer bank/shop theft, but trying to find their victims on the Web with the purpose of theft. Attackers use the hidden advances of the Internet to propose new advances, such as phishing, and deceive misfortune by using false language conditions, and accumulate their sensitive data, such as account IDs, customer names, passwords, and so on. Knowing whether a Web site page can be authenticated or phishing is an incredible problem, because its semantic-based attack structure fundamentally exploits the mistakes of the PC client. Despite the detail that product organizations dispatch new enemy of phishing items, which use heuristics, visual, and AI-based methodologies, these items can’t forestall all of the phishing assaults. In this article, an anti-phishing framework uses five different classification techniques (logistic regression, K-nearest neighbor, decision tree classifier, random forest classifier, and support vector machine) to predict phishing sites. In order to investigate the demonstration of the framework, a new dataset was collected, and preliminary results were set in it. According to the test and comparison results of the ranking estimation, the random forest calculation gives the best presentation, and the confirmation accuracy of the phishing URL is 97.10%. For validation of this outcome, confusion matrix, precision matrix, recall matrix, and ROC curve have been drawn. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

IoT and machine learning Based Smart Grid System 5

IoT and Machine Learning Based Smart Grid System

引用

5th international conference on Computer, Communication, and signal processing, ICCCSP 2021

作者： Sivasangari, A. Deepa, D. Lakshmanan L. Jesudoss A. Vignesh R. Sathyabama Institute of Science and Technology School of Computing Chennai India

ISBN: (纸本)9781665432771

the status of distribution transformers plays an important role in ensuring the secure and stable operation of these grids, owing to the large number of distribution transformers on the distribution grid. the monitoring method and process of fuzzy inference are presented. the IoT-based monitoring system consists of a subsystem for data measurement and a data reception subsystem. Firstly, by using the data measurement system, transformer signals are measured and sent to there mote server. Current power grids are experiencing a rapid transition to their more involved, versatile and intelligent smart grid counterparts, which presents enormous challenges in many ways, such as the convergence of different areas. Distributed sources of renewable energy, cyberspace defense, demand-side management, and system planning and activity decision- making. Also apply an IoT techniques and fuzzy inference machine learning algorithms to achieve more accurate result. For fulfillment of sophisticated features in the smart grid relies heavily on the Knowledge behind it. © 2021 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Mood Recognition in Online Sessions using machine learning in Realtime 5

Mood Recognition in Online Sessions using Machine Learning i...

引用

5th international conference on Computer, Communication, and signal processing, ICCCSP 2021

作者： Dey, Arnab Dasgupta, Kousik Kalyani Government Engineering College Department of Computer Science and Engineering Kalyani India

ISBN: (纸本)9781665432771

the demand of facial expression based computing is increasing day by day as this can be widely used in various sectors and are in huge demand across the globe. Human being displays their emotions through facial expressions. the aim of this research is to classify human emotions in live environment. We have implemented and trained the model using two powerful machine learning models namely Gradient Boosting (GB) Classifier and K-Nearest Neighbour (KNN) approach for classification of facial expressions with best accuracy possible. We have used combination of various datasets for this study. the efficiency of the model performed much better when we convert the image into grayscale and apply CLAHE equalization technique. We have used a novel approach for recognizing facial expressions with webcam using Gradient Boosting approach. We achieved maximum accuracy of 81% using GB Classifier and 73% utilizing K- Nearest Neighbour algorithm. the training time is faster than deep learning methods and the results prove to be efficient. the model is trained after finding the best hyper parameters. the model can be used to track the mood of the people in real-time especially during pandemic situation. © 2021 IEEE.

关键词： Adaptive boosting

来源：评论

学校读者我要写书评

暂无评论

ASVM: Adaboost with SVM-based Classifier Implementation for Aspect-based Opinion Mining to Appraise Products 5

ASVM: Adaboost with SVM-based Classifier Implementation for ...

引用

5th international conference on Inventive Computation Technologies (ICICT)

作者： Sheeba, Blesslin T. Manimegalai, T. Ananthi, S. Kamatchi, S. Anitha, G. Muthuraman, U. RMK Engn Coll Dept ECE Kavaraipettai Thiruvallur India Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India Sri Sai Ram Inst Technol Dept CSE Chennai Tamil Nadu India Jeppiaar Inst Technol Dept ECE Chennai Tamil Nadu India SIMATS Dept ECE Saveetha Sch Engn Chennai Tamil Nadu India Francis Xavier Engn Coll Dept EEE Tirunelveli India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

Product evaluations can be extremely beneficial to both customers and manufacturers. the number of evaluations could range from hundreds to thousands, with each one presenting a different point of view. It becomes increasingly difficult to analyse and extract information from existing evaluations as a result of these developments. the method of evaluating the intensity of opinionated texts is known as Sentiment Analysis. A large number of social networking sites are being utilised by users for the purpose of expressing their ideas and opinions and rating things. these user-generated opinions are much unstructured in nature, necessitating the employment of a variety of natural language processing techniques to extract meaning from them. When performing entity-based sentiment classification, the many characteristics of a item are detected throughout the training activity. For example, the picture quality, size, resolution, and memory of a camera are all important considerations. the Ensemble AdaBoost with support vector machine classifier (ASVM) is used to do the quantitative examination of each aspect of the study. In the majority of earlier publications, a product review is examined as a whole rather than taking into account each individual part of it. Due to the difficulty in identifying individual features, aspect-based opinion mining can be time-consuming and labour-intensive to perform.

关键词： Sentiment Analysis machine learning Natural Language processing AdaBoost SVM Entity Selection Ensembling

来源：评论

学校读者我要写书评

暂无评论

Modelling machine learning-based Energy Loss Detection and Monitoring System for Advanced Metering Infrastructure 5

Modelling Machine Learning-based Energy Loss Detection and M...

引用

5th international conference on Information Technology for Education and Development, ITED 2022

作者： Aniedu, Azubuike N. Nwokoye, Sandra C. Okafor, Chukwunenye S. Anyanwu, Kingley U. Nnamdi Azikiwe University Electronic and Computer Engineering Awka Nigeria

ISBN: (纸本)9781509064229

Non-technical losses (NTL) have rightly been identified as losses arising from energy generated but unaccounted for. they basically occur because of theft and other fraudulent activities surrounding illegal consumption of energy. this loss accounts for massive loss in revenue to utility companies and government and there has been concerted efforts to mitigate such abnormalities thereby saving cost. Although advanced metering infrastructure (AMI) incorporating smart meters have provided some basic organization around management of smart grids and monitoring usage information, it still fails to accurately detect NTL. In this paper therefore a solution to NTL is presented involving the deployment of support vector machines (SVM) as an underlying classifier and integrated with a real-time application interface termed Electricity Usage Classifier interface (ELUCI) for monitoring and pre-processing instantaneous electricity usage time-series data. With this configuration, a classification accuracy of 98.48% was achieved which was a 17.02% improvement over the initial classification models and with a root mean squared error (RMSE) of 0.0894 and an f-measure of 0.979. the developed system can assist governments and utilities to actively monitor and track down energy theft thereby improving revenue and avoiding economic wastages accruing from these activities. © 2022 IEEE.

关键词： Advanced metering infrastructures

来源：评论

学校读者我要写书评

暂无评论

Analytical Overview on Transfer learning in processing Dental X-rays 22

Analytical Overview on Transfer Learning in Processing Denta...

引用

5th international conference on Control and Computer Vision, ICCCV 2022

作者： Al Mokdad, Seba Al Houria, Anas Abu Talib, Manar Moufti, Mohammad Bouridane, Ahmed Nasir, Qassim College of Computing and Informatics Department of Computer Science University of Sharjah United Arab Emirates

ISBN: (纸本)9781450397315

Dental x-rays have been a standard piece of dental equipment for many years and are an indispensable diagnostic tool for dentists to detect tooth damage or disease. Recent research has focused on employing computer vision algorithms to automate analysis of dental x-rays. Our study aims to review the work done using transfer learning in dental image processing. AI solutions for dental images have been developed for many purposes, including examining tooth cavities (caries) and restorations and abnormalities in the maxillary sinuses. they have also been used to classify dental implants and determine gender in forensic studies. Transfer learning is a new approach that is being used to solve a problem that classic deep learning and machine learning techniques could not solve: that of data limitation. Our search has investigated 80 research papers, of which 30 were relevant and analyzed in this paper. the identified studies have discussed a variety of transfer learning models to process different types of x-rays and have reported their efficacy using a variety of metrics. Transfer learning was used to solve various problems depending on the research question. Some papers compared the performance of transfer learning with that of dental experts in analyzing x-ray images, the accuracy of which were surprisingly close to equal. Although the results of the majority of dental applications performed using transfer learning models are encouraging, future research will need to solve the shortcomings highlighted in the present review. © 2022 ACM.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Automatic Honey Bee Queen Presence Detection on Beehive Frames Using machine learning 11th

Automatic Honey Bee Queen Presence Detection on Beehive Fram...

引用

11th international conference on Robotics, Vision, signal processing and Power Applications, RoViSP 2021

作者： Marquis, Marie-Pier Yaddaden, Yacine Adda, Mehdi Gingras, Guillaume Corriveau-Ctôé, Michael Rimouski Canada

ISBN: (纸本)9789811681288

A honey bee queen plays a vital role in a bee colony. thus, locating it in a beehive frame is one of the important tasks performed by the beekeepers. However, searching for the queen bee with the naked eye is time consuming and its effectiveness largely depends on the visual acuity of the beekeeper. therefore, we propose two methods to automate this task using machine learning techniques. One of them is deep learning-based while the other is based on a classical method. We evaluated both methods and the obtained results are promising and highlight their efficiency. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Analysis of a Novel Integrated machine learning Model for Yield and Weather Prediction: Punjab Versus Maharashtra 5th

Analysis of a Novel Integrated Machine Learning Model for Yi...

引用

5th international conference on Recent Innovations in Computing , ICRIC 2022

作者： Chaudhary, Sarika Bajaj, Shalini Bhaskar Mongia, Shweta Amity University Haryana India MRIIRS Faridabad India

ISBN: (纸本)9789819906000

Correct prediction is a deciding factor in modern agricultural techniques to confirm food safety and sustainability faced in crop production. As radical climatic conditions affect plants growth greatly, appropriate valuation of rainfall and yield prediction can deliver a sufficient information in advance which can be utilized to maintain the crop production quality. For accurate prediction, machine learning (ML) plays a prominent role. the collaboration of agriculture system with machine learning will lead to intelligent agriculture system that helps the farmer community in their decision-making of farm management and agribusiness activities such crop management including applications on yield prediction, disease detection, weed detection, crop quality, and growth prediction. this paper implemented and analyzed a prediction model using high potential ML algorithms, viz., random forest, linear regression, Lasso regression, and support vector machine for crop yield prediction whereas KNN, decision tree, logistic regression, Gaussian Naive Bayes, SVM, and linear discriminant analysis to find the best line method for rainfall prediction. To enhance the effectiveness of pre-processing, "SSIS-an ETL" tool is utilized. the experiments laid out for the state of Maharashtra and Punjab, and dataset collected from "ICAR-Indian Agriculture Research Institute" and ***. R2-score, mean squared error, precision, recall, and F-score measure were used to ascertain the accuracy. As a result, random forest found significantly the finest algorithm for crop yield prediction by recording higher accuracy of 96.33% with maximum R2-score of 0.96 and least mean squared error (0.317), while lasso regression was found the worst with an accuracy of 32.9%. In case of rainfall prediction, Gaussian Naive Bayes secured top rank by recording considerably the highest accuracy of 91.89% as well as the maximum precision, recall, and F1-score of 0.93, 0.91, and 0.91, respectively. © 2023, the

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Research on 5G signal processing System Based on Computer Intelligent machine learning Algorithm

Research on 5G Signal Processing System Based on Computer In...

引用

IEEE international conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Ruihan Wang Shu Liu Jinyu Liu Shandong University of Science and Technology Jinan China

the data transmission opportunities cannot be fully utilized in 5G multi-user applications due to differences on channel conditions, this project intends to study an improved k-means machine learning method. then the optimal solution method based on the function between angles is proposed to make the selection of Angle parameters simple. the interface is optimized with high performance FPGA, high bandwidth and high sampling rate AD/DA. the test results show that 5Gbps fiber optic data transmission is feasible. the algorithm can intelligently select user devices for scheduling among different user devices of fairness.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Novel Resource-Constrained Insect Monitoring System based on machine Vision with Edge AI 5

A Novel Resource-Constrained Insect Monitoring System based ...

引用

5th IEEE international conference on Image processing Applications and Systems (IPAS)

作者： Kargar, Amin Wilk, Mariusz P. Zorbas, Dimitrios Gaffney, Michael T. O'Flynn, Brendan Univ Coll Cork Tyndall Natl Inst Cork Ireland Nazarbayev Univ Dept Comp Sci Nur Sultan Kazakhstan Teagasc Ashtown Food Res Ctr Hort Dev Dept Dublin Ireland

ISBN: (纸本)9781665462198

Effective insect pest monitoring is a vital component of Integrated Pest Management (IPM) strategies. It helps to support crop productivity while minimising the need for plant protection products. In recent years, many researchers have considered the integration of intelligence into such systems in the context of the Smart Agriculture research agenda. this paper describes the development of a smart pest monitoring system, developed in accordance with specific requirements associated with the agricultural sector. the proposed system is a low-cost smart insect trap, for use in orchards, that detects specific insect species that are detrimental to fruit quality. the system helps to identify the invasive insect, Brown Marmorated Stink Bug (BMSB) or Halyomorpha halys (HH) using a Microcontroller Unit-based edge device comprising of an Internet of things enabled, resource-constrained image acquisition and processing system. It is used to execute our proposed lightweight image analysis algorithm and Convolutional Neural Network (CNN) model for insect detection and classification, respectively. the prototype device is currently deployed in an orchard in Italy. the preliminary experimental results show over 70 percent of accuracy in BMSB classification on our custom-built dataset, demonstrating the proposed system feasibility and effectiveness in monitoring this invasive insect species.

关键词： machine Vision Image processing Deep learning Edge AI Integrated Pest Monitoring Food Security

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共79页 << < 33 34 35 36 37 38 39 40 41 42 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：